In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import mpl_toolkits as mplot3d
import scipy as sp
pd.set_option("display.max_columns", None)
from tqdm import tqdm
tqdm.pandas()
In [2]:
def lighten_color(color, amount=0.5):
    """
    Lightens the given color by multiplying (1-luminosity) by the given amount.
    Input can be matplotlib color string, hex string, or RGB tuple.

    Examples:
    >> lighten_color('g', 0.3)
    >> lighten_color('#F034A3', 0.6)
    >> lighten_color((.3,.55,.1), 0.5)
    """
    import matplotlib.colors as mc
    import colorsys
    try:
        c = mc.cnames[color]
    except:
        c = color
    c = colorsys.rgb_to_hls(*mc.to_rgb(c))
    return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])

DATA CLEANING AND WRANGLING¶

Typeform Data¶

In [3]:
d0 = pd.read_csv('India_riskliteracy_dataset_above median_2024_new.csv').rename({"#":"ResponseId"}, axis = 1)
d0
Out[3]:
ResponseId Unnamed: 1 #.1 Please indicate your Gender. Please mark your age (in years) What is currently your highest Education? Please state your current occupation. How do you describe your willingness to take financial risk in general? Given the number of years that you have held various investments and the amount of investing you might have done, what degree of investment experience in the stock market do you have? If an expert tries to worry or scare me, i.e. a financial advisor about my financial situation, I choose another expert. I only buy a financial product I understand. I trust doctors. When I want to buy a bigger item like a refrigerator or an expensive item of clothing, I wait a month to see whether I still want it and only buy it then. I always keep in mind that everything I do on the web could be used to my disadvantage. In my household, we/I spend: In my household, we/I _Distribution 1_\n\nHow risky do you perceive the investment to be? _Distribution 2_\n\nHow risky do you perceive the investment to be? _Distribution 3_\n\nHow risky do you perceive the investment to be? _Distribution 4_\n\nHow risky do you perceive the investment to be? _Distribution 5_\n\nHow risky do you perceive the investment to be? Distribution 6\n\nHow risky do you perceive the investment to be? Distribution 7\n\nHow risky do you perceive the investment to be? Distribution 8\n\nHow risky do you perceive the investment to be? *Mumbai * A = 9 out of 10000 *OR * B = 1 out of 1000 *Bengaluru * A = 0.7% *OR * B = 0.099% *Kolkata * A = 0.61% *OR * B = 6 out of 10000 HIV test Fingerprint DNA test Cancer screening test Professional horoscope A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%. Which of the following statements is true? There is an official prediction that the national stock market will grow 2% annually over the next 5 years. This means that… Imagine you are told that the price of the stock Soya Ruchi increases from INR 60 to INR 120 after the company merger. What does this mean? It is predicted that Indigo Bank has 30% chance of default next year. Which of the following alternatives is the most appropriate interpretation of the statement? The probability that the economy will go into a recession this year is 30%. If the economy goes into recession, the probability that the stock market will decrease is 80%. If the economy does not go into a recession, the probability that the stock market will decrease is 23%. What is the probability that the economy goes into recession given that the stock market decreased? A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from: Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000. This implies: Imagine that we flip a fair coin 1,000 times. What is your best guess about how many times the coin will come up heads in 1,000 flips? \n\n\_\_\_\_\_\_ times out of 1,000. In the Bingo Lottery, the chance of winning a $10 prize is 1%. What is your best guess about how many people will win a $10 prize if 1,000 people each buy a single ticket for Bingo Lottery?\n\n\_\_\_\_\_\_ person(s) out of 1,000. In a sweepstakes, the chance of winning a car is 1 in 1,000. What percentage of tickets for the sweepstakes wins a car?\n\n\_\_\_\_\_ % of tickets About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only] Approximately what percentage (%) of people who die from cancer die from colon cancer, breast cancer, and prostate cancer taken together? The following figure shows the number of men and women among a group of smartphone users. The total number of circles is 100. \n\nHow many more men than women are there among the 100 people using a smartphone? In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die? Please indicate your approximate annual personal income from all sources for last year Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have. uid Response Type Start Date (UTC) Stage Date (UTC) Submit Date (UTC) Network ID LTA q8_2_1 q8_2_2 q8_2_3 q8_2_4 q8_2_5 q8_3 q8_4 q8_5berlin_1 q8_5london_1 q8_5paris_1 q8_6 q8_7 q9_1_1 q9_2_1 q9_3 q10_1_1 q10_2_1 q10_3_1 q10_4 certainty1 certainty2 certainty3 certainty4 certainty5 uncertainty1 uncertainty2 numeracy1 numeracy2 numeracy3 numeracy4 numeracy5 graph1 graph2 graph3 riskcalculation1 riskcalculation2 riskcalculation3 riskcalculation4
0 00dn8csjzlekx6gzhrcv00dn8csj32tz 547 00dn8csjzlekx6gzhrcv00dn8csj32tz Male 25 - 35 Under Graduate Investment Professional, i.e. stock broker/tra... 7 10 Completely Just about Completely Completely Just about all or more than the household income, even th... have an emergency fund, and spend as it feels ... 7 7 7 6 6 6 6 7 A B A Yes Yes Yes Yes Yes The lower the quality of the study, the more l... the growth rate will be 0.4% on average each year the stock price increased by 50% 30% of the banks customers will default next year 30% it is not possible to determine which of the a... The medication increases recovery by 50% 30 20 30 9 out of 10 40 60 They are equal 5,00,000 - 15,00,000 50000 MX9oX1724844773oYI completed 2024-08-28 11:35:35 NaN 2024-08-28 12:02:05 f30fdd629e 18.402778 1 1 1 1 1 1 1 1 2 1 2 4 40 60 3 30 20 30 2 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0
1 00ujdxbfoya0donu8r00ujcjdkojc99x 428 00ujdxbfoya0donu8r00ujcjdkojc99x Female 25 - 35 Under Graduate Employee/Consultant in Other than the Finance ... 7 8 Completely Completely Completely Moderately Moderately less than half of the household income and sav... write down budget and spending, and have an em... 2 1 1 1 0 2 0 0 B B A Yes Yes Yes Yes No The higher the quality of the study, the more ... the growth rate over five years will be betwee... the stock price increased by 50% The bank will default on 30% of repayments in ... 50% 100 in 10,000 people prior to the intervention... The medication increases recovery by 50% 500 100 1 59 out of 1000 50 10 They are equal < 5,00,000 1500000 MXic21725178744djB completed 2024-09-01 08:23:04 NaN 2024-09-01 09:24:44 1c1d51349c 42.824074 1 1 1 1 2 2 3 2 2 1 2 2 50 10 3 500 100 1 3 0 0 0 0 1 0 0 1 0 1 0 0 0 0 1 1 0 0 0
2 01ro2iftnum5v4cn01r03c75aa24emhq 528 01ro2iftnum5v4cn01r03c75aa24emhq Female 18 - 25 Under Graduate Student 5 8 Just about Completely Completely Completely Just about more than half of the household income and sav... write down budget and spending, and have an em... 6 6 2 6 6 6 2 4 A B B Yes Yes No Yes Yes The lower the quality of the study, the more l... the growth rate over five years will be exactl... an answer is not possible based on the informa... Banks similar to Indigo will default 30% of th... 50% 70 in 100 people prior to the intervention to ... The medication increases recovery by 100% 46 100 99 59 out of 1000 79 40 Hertinol 5,00,000 - 15,00,000 500000 MX7wI1724844822zNm completed 2024-08-28 11:36:50 NaN 2024-08-28 11:51:07 81a8a6836a 9.918981 1 1 2 1 1 1 2 1 2 2 1 3 79 40 2 46 100 99 3 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0
3 022xoawhrghfhv4a1g022xmz7hb0m41f 442 022xoawhrghfhv4a1g022xmz7hb0m41f Female 46 - 55 Under Graduate Entrepreneur or Own Business 5 4 Somewhat Somewhat Moderately Just about Somewhat all or more than the household income, even th... spend as it feel right, and do not have an eme... 4 4 4 4 4 4 4 4 B A B Yes Yes Yes No No The lower the quality of the study, the more l... the growth rate will be 0.4% on average each year the stock price increased by 60% 30% of the banks customers will default next year 80% 5 in 100 people prior to the intervention to 6... The medication increases recovery by 2% 500 1 1 59 out of 1000 17 65 They are equal < 5,00,000 15000000 MXFDH1725178659xRn completed 2024-09-01 08:21:14 NaN 2024-09-01 08:33:49 fbd8962401 8.738426 1 1 1 2 2 1 1 2 1 2 3 1 17 65 3 500 1 1 3 0 0 0 1 1 1 0 1 1 0 0 0 0 0 1 1 0 0 0
4 02pxtdbyibecqqfvwlw02pxwfbane9zd 558 02pxtdbyibecqqfvwlw02pxwfbane9zd Female 46 - 55 Post Graduate Employee/Consultant in Other than the Finance ... 6 10 Not at all Completely Completely Completely Moderately all or more than the household income, even th... have an emergency fund, and spend as it feels ... 5 5 6 5 7 5 0 5 B A A Yes Yes Yes No Yes The higher the quality of the study, the more ... it is not possible to predict the growth rate ... an answer is not possible based on the informa... 30% of the banks customers will default next year 30% 5 in 100 people prior to the intervention to 6... None of the above is implied. 1000 1 100 9 out of 10 20 20 Can’t say 5,00,000 - 15,00,000 3000000 MXIpM1724844742xet completed 2024-08-28 11:35:05 NaN 2024-08-28 11:48:47 e8b668d11b 9.513889 1 1 1 2 1 2 4 2 1 1 4 1 20 20 4 1000 1 100 2 0 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
638 zeoufbqlngfunzzeoymhtj2sp5cw6lur 396 zeoufbqlngfunzzeoymhtj2sp5cw6lur Male 25 - 35 Post Graduate Investment Professional, i.e. stock broker/tra... 3 8 Not at all Somewhat Somewhat Moderately Somewhat less than half of the household income and sav... spend as it feel right, and do not have an eme... 3 4 4 3 6 6 3 4 B A A No Yes No No No The higher the quality of the study, the more ... the growth rate over five years will be exactl... the stock price increased by 100% Banks similar to Indigo will default 30% of th... 50% 70 in 100 people prior to the intervention to ... The medication increases recovery by 100% 500 100 1 59 out of 1000 50 40 Crosicol < 5,00,000 50000 MXFoD1725258821KAh completed 2024-09-02 06:36:01 NaN 2024-09-02 06:46:38 c31fa70c8d 7.372685 2 1 2 2 2 2 2 2 1 1 1 3 50 40 1 500 100 1 3 1 0 1 1 1 0 0 1 1 1 1 0 0 0 0 1 0 0 0
639 zfemo30rg0ekva18x1kjwzfemo2wpg2p 185 zfemo30rg0ekva18x1kjwzfemo2wpg2p Male 36 - 45 Ph.D. or higher Employee/Consultant in Other than the Finance ... 6 8 Not at all Moderately Just about Not at all Not at all less than half of the household income and sav... have an emergency fund, and spend as it feels ... 3 1 4 2 0 5 0 2 B A A No Yes Yes No No Irrespective of the quality of the study, futu... the growth rate over five years will be betwee... the stock price increased by 100% 30% of central bankers think that Indigo Bank ... 30% 100 in 10,000 people prior to the intervention... The medication increases recovery by 100% 500 10 1 59 out of 1000 25 20 Can’t say 15,00,001 - 25,00,000 8500000 MXUxt1725260651heG completed 2024-09-02 07:08:18 NaN 2024-09-02 07:23:09 fbb956cffc 10.312500 2 1 1 2 2 4 3 2 1 1 1 2 25 20 4 500 10 1 3 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 1 1 0 0
640 znqvw4t38br3072znqvwvjsgb6wvj7nt 305 znqvw4t38br3072znqvwvjsgb6wvj7nt Female 36 - 45 Post Graduate Entrepreneur or Own Business 5 5 Somewhat Completely Just about Moderately Somewhat less than half of the household income and sav... spend as it feel right, and do not have an eme... 0 0 0 1 0 0 0 0 B B B Yes Yes Yes No No Irrespective of the quality of the study, futu... it is not possible to predict the growth rate ... the stock price increased by 50% 30% of the banks customers will default next year 80% it is not possible to determine which of the a... The medication increases recovery by 2% 500 10 1 9 out of 59 25 20 Hertinol 5,00,000 - 15,00,000 2000000 MXd921725259652XDJ completed 2024-09-02 06:49:56 NaN 2024-09-02 07:17:20 1964f49e1c 19.027778 1 1 1 2 2 3 4 2 2 2 3 4 25 20 2 500 10 1 1 0 0 0 1 1 0 1 1 0 0 0 1 1 1 0 1 1 0 1
641 zstpq23h1x3ilab8s8vsrbkw8t4zstpq 306 zstpq23h1x3ilab8s8vsrbkw8t4zstpq Male 18 - 25 Under Graduate Entrepreneur or Own Business 6 9 Completely Completely Moderately Completely Moderately all or more than our household income, because... have an emergency fund, and spend as it feels ... 6 3 7 1 2 7 7 3 A A B No Yes Yes No No The higher the quality of the study, the more ... the growth rate over five years will be betwee... the stock price increased by 60% Banks similar to Indigo will default 30% of th... 80% 5 in 100 people prior to the intervention to 6... None of the above is implied. 750 10 1 59 out of 1000 50 10 They are equal 5,00,000 - 15,00,000 60000000 MXfmg1725259705Fkj completed 2024-09-02 06:49:52 NaN 2024-09-02 07:01:04 03aa77ec48 7.777778 2 1 1 2 2 2 3 1 1 2 4 1 50 10 3 750 10 1 3 1 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0
642 ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9 95 ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9 Male 25 - 35 Post Graduate Entrepreneur or Own Business 6 7 Somewhat Just about Completely Moderately Somewhat more than half of the household income and sav... spend as it feel right, and do not have an eme... 6 0 2 5 3 7 0 4 B A A No Yes Yes Yes Yes The higher the quality of the study, the more ... it is not possible to predict the growth rate ... the stock price increased by 100% 30% of central bankers think that Indigo Bank ... 80% 5 in 100 people prior to the intervention to 6... The medication increases recovery by 2% 500 10 1 59 out of 1000 25 60 Can’t say 15,00,001 - 25,00,000 5000000 MXjp51725955476hMt completed 2024-09-10 08:06:34 NaN 2024-09-10 08:16:35 f68294192e 6.956019 2 1 1 1 1 2 4 2 1 1 3 1 25 60 4 500 10 1 3 1 0 0 0 0 0 1 1 1 1 0 0 1 0 0 1 1 0 0

643 rows × 93 columns

Demographics¶

In [4]:
# Age groups/rec = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 55 y/o
# Age group 4 = 56 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)

d0["age_rec"] = np.where( d0["Please mark your age (in years)"] == "18 - 25" , 2,
                          np.where(d0["Please mark your age (in years)"] == "25 - 35", 2,
                                   np.where( d0["Please mark your age (in years)"] == "36 - 45", 3,
                                            np.where( d0["Please mark your age (in years)"] == "46 - 55", 3,
                                                     np.where( d0["Please mark your age (in years)"] == "56 - 65", 4,
                                                              np.where( d0["Please mark your age (in years)"] == "Above 65", 4, 0
                                                                      )
                                                             )
                                                    )
                                           )
                                  )
                        )

d0["age"] = np.where( d0["Please mark your age (in years)"] == "18 - 25" , (18 + 25) / 2,
                          np.where(d0["Please mark your age (in years)"] == "25 - 35", (26 + 35) / 2,
                                   np.where( d0["Please mark your age (in years)"] == "36 - 45", (36 + 45) / 2,
                                            np.where( d0["Please mark your age (in years)"] == "46 - 55", (46 + 55) / 2,
                                                     np.where( d0["Please mark your age (in years)"] == "56 - 65", (56 + 65) / 2,
                                                              np.where( d0["Please mark your age (in years)"] == "Above 65", (66 + 75) / 2, 0
                                                                      )
                                                             )
                                                    )
                                           )
                                  )
                        )


# Secondary upto 10 – ISCED 3
# Senior Secondary upto 12 - ISCED 3 
# Diploma and voca – ISCED 2
# UG – ISCED 2
# PG – ISCED 1
# PhD and higher – ISCED 1

d0["isced"] = np.where(d0["What is currently your highest Education?"] == "Post Graduate", 1,
                        np.where(d0["What is currently your highest Education?"] == "Under Graduate", 2,
                                 np.where(d0["What is currently your highest Education?"] == "Ph.D. or higher", 1,
                                          np.where(d0["What is currently your highest Education?"] == "Diploma or vocation training", 2,
                                                   np.where(d0["What is currently your highest Education?"] == "School degree (X or XII)", 3, 0
                                                           )
                                                  )
                                         )
                                )
                       )

d0["income"] = np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "< 5,00,000", 1,
                         np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "5,00,000 - 15,00,000", 2,
                                  np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "15,00,001 - 25,00,000", 3,
                                           np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "25,00,001 - 35,00,000", 4,
                                                    np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "35,00,001 - 45,00,000", 5,
                                                             np.where( d0["Please indicate your approximate annual personal income from all sources for last year"] == "> 45,00,000", 6,0
                                                                     )
                                                            )
                                                   )
                                           )
                                  )
                         )

d0["wealth"] = d0["Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have."]
In [ ]:
In [ ]:

Scoring¶

In [5]:
d0["Certainty_3"] = d0["certainty1"] + d0["certainty2"] + d0["certainty3"]
d0["RiskComprehension_3"] = d0["riskcalculation1"] + d0["riskcalculation2"] + d0["riskcalculation4"]
d0["GraphLiteracy_3"] = d0["graph1"] + d0["graph2"] + d0["graph3"]
d0["Numeracy_3"] = d0["numeracy1"] + d0["numeracy2"] + d0["numeracy3"]
d0["Bayesianreasoning_1"] = d0["numeracy4"]
d0["TotalScore_13"] = d0["Certainty_3"] + d0["RiskComprehension_3"] + d0["GraphLiteracy_3"] + d0["Numeracy_3"] + d0["Bayesianreasoning_1"]

d0["Certainty_%"] = d0["Certainty_3"] / 3 * 100
d0["RiskComprehension_%"] = d0["RiskComprehension_3"] / 3 * 100
d0["GraphLiteracy_%"] = d0["GraphLiteracy_3"] / 3 * 100
d0["Numeracy_%"] = d0["Numeracy_3"] / 3 * 100
d0["Bayesianreasoning_%"] = d0["Bayesianreasoning_1"] / 1 * 100

d0["TotalScore_%"] = d0["TotalScore_13"] / 13 * 100


colReq = ["ResponseId", "age", "age_rec", "isced", "income", "wealth", "Certainty_3", "RiskComprehension_3", "GraphLiteracy_3",
          "Numeracy_3", "Bayesianreasoning_1", "Certainty_%", "RiskComprehension_%", "GraphLiteracy_%", "Numeracy_%", "Bayesianreasoning_%",
          "TotalScore_13", "TotalScore_%",]

df2 = d0[colReq].copy()
df2
Out[5]:
ResponseId age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_%
0 00dn8csjzlekx6gzhrcv00dn8csj32tz 30.5 2 2 2 50000 0 0 1 1 0 0.000000 0.000000 33.333333 33.333333 0.0 2 15.384615
1 00ujdxbfoya0donu8r00ujcjdkojc99x 30.5 2 2 1 1500000 0 1 1 2 0 0.000000 33.333333 33.333333 66.666667 0.0 4 30.769231
2 01ro2iftnum5v4cn01r03c75aa24emhq 21.5 2 2 2 500000 1 0 0 0 1 33.333333 0.000000 0.000000 0.000000 100.0 2 15.384615
3 022xoawhrghfhv4a1g022xmz7hb0m41f 50.5 3 2 1 15000000 0 1 1 2 0 0.000000 33.333333 33.333333 66.666667 0.0 4 30.769231
4 02pxtdbyibecqqfvwlw02pxwfbane9zd 50.5 3 1 2 3000000 0 0 1 3 0 0.000000 0.000000 33.333333 100.000000 0.0 4 30.769231
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
638 zeoufbqlngfunzzeoymhtj2sp5cw6lur 30.5 2 1 1 50000 2 1 0 3 1 66.666667 33.333333 0.000000 100.000000 100.0 7 53.846154
639 zfemo30rg0ekva18x1kjwzfemo2wpg2p 40.5 3 1 3 8500000 1 2 2 3 1 33.333333 66.666667 66.666667 100.000000 100.0 9 69.230769
640 znqvw4t38br3072znqvwvjsgb6wvj7nt 40.5 3 1 2 2000000 0 3 2 1 0 0.000000 100.000000 66.666667 33.333333 0.0 6 46.153846
641 zstpq23h1x3ilab8s8vsrbkw8t4zstpq 21.5 2 2 2 60000000 1 1 1 1 0 33.333333 33.333333 33.333333 33.333333 0.0 4 30.769231
642 ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9 30.5 2 1 3 5000000 1 2 1 3 0 33.333333 66.666667 33.333333 100.000000 0.0 7 53.846154

643 rows × 18 columns

In [ ]:
In [ ]:

GRAPHS¶

In [6]:
# The following graph represents frequency of each data point on "TotalScore_13" or the total score out of 19 questions across the sample.

df2['TotalScore_13'].plot(kind = 'hist', xticks = np.arange(0, 15, step=1), xlabel = 'TotalScore_13', title = 'Frequency of Scores' )
Out[6]:
<Axes: title={'center': 'Frequency of Scores'}, xlabel='TotalScore_13', ylabel='Frequency'>
No description has been provided for this image
In [7]:
# Absolute mean scores for each facet

(df2[['Certainty_3','RiskComprehension_3','GraphLiteracy_3','Numeracy_3','Bayesianreasoning_1','TotalScore_13']].mean(axis = 0)).plot(kind = 'bar', title = 'Absolute mean of scores for above data set')
Out[7]:
<Axes: title={'center': 'Absolute mean of scores for above data set'}>
No description has been provided for this image
In [8]:
# Normalised mean scores for each facet

df2[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean(axis = 0).plot(kind = 'bar', title = 'Normalised mean of scores for above data set')
Out[8]:
<Axes: title={'center': 'Normalised mean of scores for above data set'}>
No description has been provided for this image
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [9]:
# DF3 = Sorted by income

df3 = df2.sort_values(by = 'income')
df3 = df3.reset_index(drop = True)
df3.drop(df3[df3['income'] == 7].index, inplace = True)

df3
Out[9]:
ResponseId age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_%
0 ingapx8z0r0iwgemwuiiingaf2dww17d 21.5 2 2 1 500000 0 0 1 3 0 0.000000 0.000000 33.333333 100.000000 0.0 4 30.769231
1 75j5pullab0sviophrp0hc75j5pulim7 40.5 3 1 1 25000 0 2 2 3 0 0.000000 66.666667 66.666667 100.000000 0.0 7 53.846154
2 77fnlcmnn3dd87xjjuo77fnlcmed8um0 21.5 2 2 1 400000 1 3 1 2 1 33.333333 100.000000 33.333333 66.666667 100.0 8 61.538462
3 s0p9cmpv4i57ggdrg5s0p9celrn67f5t 30.5 2 2 1 10000 0 2 0 2 1 0.000000 66.666667 0.000000 66.666667 100.0 5 38.461538
4 qt4btily2rqt6phgfaldqt4btixo2oe9 30.5 2 1 1 600000 0 0 2 1 0 0.000000 0.000000 66.666667 33.333333 0.0 3 23.076923
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
638 gs539g4qtok8tm7rmcjegs539g7ozj75 50.5 3 1 6 10000000 1 2 1 2 0 33.333333 66.666667 33.333333 66.666667 0.0 6 46.153846
639 jajunac3e3j0oukoghbjajud9u72ogvy 50.5 3 1 6 25000000 0 1 1 1 0 0.000000 33.333333 33.333333 33.333333 0.0 3 23.076923
640 j6fzpylzfvgvsej6fzpyd0uzp0f1th8x 30.5 2 1 6 100000000 0 1 3 1 0 0.000000 33.333333 100.000000 33.333333 0.0 5 38.461538
641 f14uszmzhoujwobpkif14uszd4f33ehs 50.5 3 1 6 500000 2 0 2 0 0 66.666667 0.000000 66.666667 0.000000 0.0 4 30.769231
642 x292ml1l4xbxk1apxczo1x292ml1chrx 30.5 2 1 6 10000000 1 1 2 1 0 33.333333 33.333333 66.666667 33.333333 0.0 5 38.461538

643 rows × 18 columns

In [10]:
# Absolute mean score for each facet (Sort by Income)

df3[['Certainty_3','RiskComprehension_3','GraphLiteracy_3','Numeracy_3','Bayesianreasoning_1','TotalScore_13']].mean(axis = 0).plot(kind = 'bar', title = 'Absolute mean score for each facet (Sort by Income)')
Out[10]:
<Axes: title={'center': 'Absolute mean score for each facet (Sort by Income)'}>
No description has been provided for this image
In [11]:
# Normalised mean score for each facet (Sort by Income)

(df3[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean(axis = 0)).plot(kind = 'bar', title = 'Normalised mean score for each facet (Sort by Income)')
Out[11]:
<Axes: title={'center': 'Normalised mean score for each facet (Sort by Income)'}>
No description has been provided for this image
In [12]:
# Normalised mean of each facet as a numerical (sorted by income)

df3.groupby('income')[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean()
Out[12]:
Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_%
income
1 21.066667 35.733333 37.600000 60.000000 23.200000 37.415385
2 18.505338 41.992883 42.586002 57.532622 25.978648 39.063783
3 21.763085 41.046832 36.639118 59.228650 24.793388 38.525111
4 21.739130 38.405797 32.608696 56.521739 23.913043 36.287625
5 18.518519 35.185185 42.592593 49.074074 36.111111 36.324786
6 18.627451 41.176471 38.235294 50.980392 26.470588 36.425339
In [13]:
# Count of responses for each category (sorted by income)

df3.groupby('income')[['ResponseId']].count()
Out[13]:
ResponseId
income
1 125
2 281
3 121
4 46
5 36
6 34
In [14]:
# Absolute mean of Total Facet score line plot (sorted by income)

df3.groupby('income')[['TotalScore_13']].mean().plot( kind = 'line', title = 'Absolute mean of Total Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[14]:
<matplotlib.legend.Legend at 0x1f817aa8c90>
No description has been provided for this image
In [15]:
# Absolute mean of each Facet score line plot (sorted by income)

df3.groupby('income')[['Certainty_3','RiskComprehension_3','GraphLiteracy_3','Numeracy_3','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[15]:
<matplotlib.legend.Legend at 0x1f817ad09d0>
No description has been provided for this image
In [16]:
# Normalised mean of each Facet score line plot (sorted by income)


df3.groupby('income')[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%', 'TotalScore_%']].mean().plot( kind = 'line', title = 'Normalised mean of each Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[16]:
<matplotlib.legend.Legend at 0x1f817621b90>
No description has been provided for this image
In [17]:
# Normalised mean of each Facet score stacked plot (sorted by income)

df3.groupby('income')[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = 'Normalised mean of each Facet score stacked plot (sorted by income)', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[17]:
<matplotlib.legend.Legend at 0x1f8173a6710>
No description has been provided for this image
In [18]:
# Normalised mean of each Facet score hist plot (sorted by income)

df3.groupby('income')[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each wealth response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[18]:
<matplotlib.legend.Legend at 0x1f817af09d0>
No description has been provided for this image
In [19]:
# Trend line for Absolute Total Facet Score vs income

sns.regplot (data = df3, x = 'income', y = 'TotalScore_13')
Out[19]:
<Axes: xlabel='income', ylabel='TotalScore_13'>
No description has been provided for this image
In [20]:
# Trend line for Absolute Independent Facet Score vs income reponses

fig, ax = plt.subplots(figsize=(6, 6))

sns.regplot (data = df3, x = 'income', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax, label='Certainty_3')
sns.regplot (data = df3, x = 'income', y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax, label='RiskComprehension_3')
sns.regplot (data = df3, x = 'income', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax, label='Numeracy_3')
sns.regplot (data = df3, x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax, label='GraphLiteracy_3')
sns.regplot (data = df3, x = 'income', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax, label='Bayesianreasoning_1' )

ax.set(ylabel='Scores', xlabel='income')
ax.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [21]:
# Violine Plot for TotalScore_13 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'TotalScore_13')
Out[21]:
<Axes: xlabel='income', ylabel='TotalScore_13'>
No description has been provided for this image
In [22]:
# Violine Plot for Certainty_3 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Certainty_3')
Out[22]:
<Axes: xlabel='income', ylabel='Certainty_3'>
No description has been provided for this image
In [23]:
# Violine Plot for RiskComprehension_3 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'RiskComprehension_3')
Out[23]:
<Axes: xlabel='income', ylabel='RiskComprehension_3'>
No description has been provided for this image
In [24]:
# Violine Plot for GraphLiteracy_3 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'GraphLiteracy_3')
Out[24]:
<Axes: xlabel='income', ylabel='GraphLiteracy_3'>
No description has been provided for this image
In [25]:
# Violine Plot for Numeracy_3 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Numeracy_3')
Out[25]:
<Axes: xlabel='income', ylabel='Numeracy_3'>
No description has been provided for this image
In [26]:
# Violine Plot for TotalScore_13 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Bayesianreasoning_1')
Out[26]:
<Axes: xlabel='income', ylabel='Bayesianreasoning_1'>
No description has been provided for this image
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [27]:
# Data Frame 2 or df2 is an aggregate data on facet total scores and over all total score, along with wealth and income data vs each response. 
# This data set is also sorted by wealth with all responses 'NA' removed.
# We also assign quartiles ranking each response according to this sort.

df4 = df2.sort_values(by = 'wealth')
df4 = df4.reset_index(drop = True)
df4 = df4.dropna(axis = 0, subset = 'wealth')
df4.insert(loc = len(df4.columns), column = "Quartile Number", value = pd.qcut(df4["wealth"],q = 4, labels = False ) + 1, allow_duplicates = 'False')

df4
Out[27]:
ResponseId age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_% Quartile Number
0 2rkur9ckjvsyz36zmayz1zw2rkur9ckn 40.5 3 1 3 0 1 0 0 2 0 33.333333 0.000000 0.000000 66.666667 0.0 3 23.076923 1
1 e7ty2tbwa1hcmte7ty683aw64pla7689 30.5 2 2 1 0 0 1 1 1 0 0.000000 33.333333 33.333333 33.333333 0.0 3 23.076923 1
2 y139mcxc619h0ooosy139muk14mpfpvm 30.5 2 3 1 0 3 1 0 2 0 100.000000 33.333333 0.000000 66.666667 0.0 6 46.153846 1
3 pz369rewa2ej3jpz39du46dqedgpnaem 21.5 2 1 2 2 1 1 3 3 0 33.333333 33.333333 100.000000 100.000000 0.0 8 61.538462 1
4 6lyvarwdyk7v7hx2qgs6lyyfqalmor05 30.5 2 1 1 2 2 1 0 1 0 66.666667 33.333333 0.000000 33.333333 0.0 4 30.769231 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
638 35zux4sc18rplyz3dc85z35zux4sdfm4 21.5 2 2 4 500000000 1 1 0 1 0 33.333333 33.333333 0.000000 33.333333 0.0 3 23.076923 4
639 wn9mk6m1k8o10l1twn9mk6b4z7l7yvhc 50.5 3 1 3 500000000 1 2 3 3 0 33.333333 66.666667 100.000000 100.000000 0.0 9 69.230769 4
640 oczyh23wpxpk4o0teoczy5zzzng3i6kk 30.5 2 2 6 500000000 1 2 3 3 0 33.333333 66.666667 100.000000 100.000000 0.0 9 69.230769 4
641 4udfypw7lf8mmgjmxr4d4udfym4sx1sf 40.5 3 1 3 500000000 0 0 0 2 1 0.000000 0.000000 0.000000 66.666667 100.0 3 23.076923 4
642 zcj7ldokhyo6217f9sriwizcj7ldodwt 40.5 3 2 3 600000000 1 3 1 1 0 33.333333 100.000000 33.333333 33.333333 0.0 6 46.153846 4

643 rows × 19 columns

In [28]:
# Absolute mean score for each facet (Sort by Wealth)

df4[['Certainty_3','RiskComprehension_3','Numeracy_3','GraphLiteracy_3','Bayesianreasoning_1','TotalScore_13']].mean(axis = 0).plot(kind = 'bar', title = 'Absolute mean score for each facet (Sort by Wealth)')
Out[28]:
<Axes: title={'center': 'Absolute mean score for each facet (Sort by Wealth)'}>
No description has been provided for this image
In [29]:
# Normalised mean score for each facet (Sort by Wealth)

(df4[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean(axis = 0)).plot(kind = 'bar', title = 'Normalised mean score for each facet (Sort by Wealth)')
Out[29]:
<Axes: title={'center': 'Normalised mean score for each facet (Sort by Wealth)'}>
No description has been provided for this image
In [30]:
# Line Plot of Absolute Total Facet score vs sorted wealth

df4.plot(use_index = 'True' , kind = 'line', y = ['TotalScore_13'], title = 'Total responses line plot sorted by Wealth',xlabel = 'Unique responses sorted by wealth', ylabel = 'TotalScore_13')
Out[30]:
<Axes: title={'center': 'Total responses line plot sorted by Wealth'}, xlabel='Unique responses sorted by wealth', ylabel='TotalScore_13'>
No description has been provided for this image
In [31]:
# Absolute Facet scores line plot from low to high wealth 
# I do understand that this is non readable

df4[['Certainty_3','RiskComprehension_3','Numeracy_3','GraphLiteracy_3','Bayesianreasoning_1']].plot( kind = 'line', title = 'Absolute Facet scores line plot', use_index = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[31]:
<matplotlib.legend.Legend at 0x1f81a52d310>
No description has been provided for this image
In [32]:
# Trend line for Absolute Total Facet Score vs unique wealth reponses

sns.regplot (data = df4, x = df4.index, y = 'TotalScore_13')
Out[32]:
<Axes: ylabel='TotalScore_13'>
No description has been provided for this image
In [33]:
# Trend line for Absolute Independent Facet Score vs wealth reponses

fig1, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = df4.index, y = 'Certainty_3', fit_reg=True, ci=None, ax=ax1, label='Certainty_3')
sns.regplot (data = df4, x = df4.index, y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax1, label='RiskComprehension_3')
sns.regplot (data = df4, x = df4.index, y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax1, label='Numeracy_3')
sns.regplot (data = df4, x = df4.index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax1, label='GraphLiteracy_3')
sns.regplot (data = df4, x = df4.index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax1, label='Bayesianreasoning_1' )

ax1.set(ylabel='Scores', xlabel='wealth')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [34]:
# Count of number of responses under each quartile

df4.groupby('Quartile Number')[['Quartile Number']].count()
Out[34]:
Quartile Number
Quartile Number
1 161
2 162
3 159
4 161
In [35]:
df4.loc[df4['Quartile Number']==1]
Out[35]:
ResponseId age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_% Quartile Number
0 2rkur9ckjvsyz36zmayz1zw2rkur9ckn 40.5 3 1 3 0 1 0 0 2 0 33.333333 0.000000 0.000000 66.666667 0.0 3 23.076923 1
1 e7ty2tbwa1hcmte7ty683aw64pla7689 30.5 2 2 1 0 0 1 1 1 0 0.000000 33.333333 33.333333 33.333333 0.0 3 23.076923 1
2 y139mcxc619h0ooosy139muk14mpfpvm 30.5 2 3 1 0 3 1 0 2 0 100.000000 33.333333 0.000000 66.666667 0.0 6 46.153846 1
3 pz369rewa2ej3jpz39du46dqedgpnaem 21.5 2 1 2 2 1 1 3 3 0 33.333333 33.333333 100.000000 100.000000 0.0 8 61.538462 1
4 6lyvarwdyk7v7hx2qgs6lyyfqalmor05 30.5 2 1 1 2 2 1 0 1 0 66.666667 33.333333 0.000000 33.333333 0.0 4 30.769231 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
156 qqsa84yklzt7mi9qqsa9kp15tsjflbpn 30.5 2 2 2 350000 0 1 1 2 1 0.000000 33.333333 33.333333 66.666667 100.0 5 38.461538 1
157 sm67z605u7x7he9zjss0qdbjsm67z6wc 21.5 2 1 3 350000 1 3 1 0 0 33.333333 100.000000 33.333333 0.000000 0.0 5 38.461538 1
158 syhai9qvq3md52xkssyha58qxhgyx7uo 50.5 3 1 1 350000 0 1 2 2 0 0.000000 33.333333 66.666667 66.666667 0.0 5 38.461538 1
159 p7ung37hpf0ejr08izmp7u2wron7wo0a 50.5 3 2 2 350000 1 1 2 3 0 33.333333 33.333333 66.666667 100.000000 0.0 7 53.846154 1
160 iywefc200664a8s19oq3viywefc2zav5 40.5 3 1 2 350000 2 1 2 0 0 66.666667 33.333333 66.666667 0.000000 0.0 5 38.461538 1

161 rows × 19 columns

In [36]:
# This graph is basically a line plot of the TotalScore_13 across first quartile unique responses which are ordered by income

df4.loc[df4['Quartile Number']==1].plot(use_index = 'True' , kind = 'line', y = ['TotalScore_13'], title = 'Total Score line plot sorted by Wealth Q1',xlabel = 'Unique responses', ylabel = 'TotalScore_13')
Out[36]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q1'}, xlabel='Unique responses', ylabel='TotalScore_13'>
No description has been provided for this image
In [37]:
# Trend line for Absolute Total Facet Score vs unique Q14. Income reponses in Quartile 1

sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'TotalScore_13')
Out[37]:
<Axes: ylabel='TotalScore_13'>
No description has been provided for this image
In [38]:
# Trend line for Absolute Independent Facet Score vs wealth reponses in Quartile 1

fig1, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Certainty_3', fit_reg=True, ci=None, ax=ax2, label='Certainty_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax2, label='RiskComprehension_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax2, label='Numeracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax2, label='GraphLiteracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax2, label='Bayesianreasoning_1' )

ax2.set(ylabel='Scores', xlabel='wealth')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [39]:
df4.loc[df4['Quartile Number']==2]
Out[39]:
ResponseId age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_% Quartile Number
161 pjshk87e5oqn7cvxupjshkblv1rdybkl 40.5 3 3 1 357000 0 2 2 3 0 0.000000 66.666667 66.666667 100.000000 0.0 7 53.846154 2
162 mm9er7zx3n18o3lmlgmm9erc50lbg3qs 40.5 3 1 1 375000 0 2 0 1 0 0.000000 66.666667 0.000000 33.333333 0.0 3 23.076923 2
163 ht7ctpj695gzyrjbt8adwu5eht7ctpj6 40.5 3 2 1 380000 0 2 0 1 0 0.000000 66.666667 0.000000 33.333333 0.0 3 23.076923 2
164 nomuxcrlfa5gta9qndnomuxcvravzzyh 40.5 3 2 1 380000 0 2 0 2 0 0.000000 66.666667 0.000000 66.666667 0.0 4 30.769231 2
165 k2rqe0t5rjl9kb0pk2rqeqqojp9lytra 30.5 2 1 2 399000 2 1 1 1 0 66.666667 33.333333 33.333333 33.333333 0.0 5 38.461538 2
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
318 kkrvshmue24jtdtwj8qkkrvd0jnsfbgx 30.5 2 1 3 1200000 0 0 1 1 1 0.000000 0.000000 33.333333 33.333333 100.0 3 23.076923 2
319 5zf57p6w5606c4ifg3j5zf57pc53olwd 30.5 2 1 2 1200000 0 1 1 0 0 0.000000 33.333333 33.333333 0.000000 0.0 2 15.384615 2
320 8osegw7cma9bwcnp1rze8os0i7fdhvm7 30.5 2 1 2 1200000 0 2 3 3 0 0.000000 66.666667 100.000000 100.000000 0.0 8 61.538462 2
321 24leb8928zk3nvdsb6gll24lebvjrvvk 30.5 2 1 6 1200000 0 3 1 3 0 0.000000 100.000000 33.333333 100.000000 0.0 7 53.846154 2
322 9womeewucr9fx6d29womedb3b0jtnum6 30.5 2 1 2 1200000 1 1 0 1 0 33.333333 33.333333 0.000000 33.333333 0.0 3 23.076923 2

162 rows × 19 columns

In [40]:
# This graph is basically a line plot of the TotalScore_13 across second quartile unique responses which are ordered by income

df4.loc[df4['Quartile Number']==2].plot(use_index = 'True' , kind = 'line', y = ['TotalScore_13'], title = 'Total Score line plot sorted by Wealth Q1',xlabel = 'Unique responses', ylabel = 'TotalScore_13')
Out[40]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q1'}, xlabel='Unique responses', ylabel='TotalScore_13'>
No description has been provided for this image
In [41]:
# Trend line for Absolute Total Facet Score vs unique wealth reponses in Quartile 2

sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'TotalScore_13')
Out[41]:
<Axes: ylabel='TotalScore_13'>
No description has been provided for this image
In [42]:
# Trend line for Absolute Independent Facet Score vs wealth reponses in Quartile 2

fig1, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Certainty_3', fit_reg=True, ci=None, ax=ax3, label='Certainty_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax3, label='RiskComprehension_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax3, label='Numeracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax3, label='GraphLiteracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax3, label='Bayesianreasoning_1' )

ax3.set(ylabel='Scores', xlabel='wealth')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [43]:
df4.loc[df4['Quartile Number']==3]
Out[43]:
ResponseId age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_% Quartile Number
323 7vxwfvymsyuult211ipu7vxwfvqhdcq7 50.5 3 2 2 1250000 1 1 0 2 0 33.333333 33.333333 0.000000 66.666667 0.0 4 30.769231 3
324 z1yuu5oonxkvix25a7z1yuugo790vc38 21.5 2 2 2 1250000 0 2 1 3 1 0.000000 66.666667 33.333333 100.000000 100.0 7 53.846154 3
325 odfxscnhx6pz1ptrodfxs46gbnyv7ntb 60.5 4 1 2 1250000 0 1 2 1 1 0.000000 33.333333 66.666667 33.333333 100.0 5 38.461538 3
326 mck3u15th9xjqtg40mck301n03zilmrq 30.5 2 1 3 1280000 1 0 0 0 0 33.333333 0.000000 0.000000 0.000000 0.0 1 7.692308 3
327 lmf40pcjk28ect1hxa0xlmf40phmawgq 30.5 2 1 3 1300000 1 0 0 2 0 33.333333 0.000000 0.000000 66.666667 0.0 3 23.076923 3
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
477 gpm3fq7g5uy9k9zttsvgpm3fkzsitz4q 30.5 2 1 2 5000000 0 1 3 3 0 0.000000 33.333333 100.000000 100.000000 0.0 7 53.846154 3
478 bg1xhvcrxugb6h19vbg1psootbgcpo45 40.5 3 2 2 5000000 0 2 1 3 1 0.000000 66.666667 33.333333 100.000000 100.0 7 53.846154 3
479 e0ingnzzvib38goe0ingbhmr92mkjker 30.5 2 1 3 5000000 2 2 0 0 1 66.666667 66.666667 0.000000 0.000000 100.0 5 38.461538 3
480 nvwubouqvv6a9psx6nvwub352o8bqowj 21.5 2 1 5 5000000 0 1 2 2 1 0.000000 33.333333 66.666667 66.666667 100.0 6 46.153846 3
481 obftinjncjw2fkari22ghobfti6alubg 30.5 2 1 3 5200000 0 1 3 3 0 0.000000 33.333333 100.000000 100.000000 0.0 7 53.846154 3

159 rows × 19 columns

In [44]:
# This graph is basically a line plot of the TotalScore_13 across third quartile unique responses which are ordered by wealth

df4.loc[df4['Quartile Number']==3].plot(use_index = 'True' , kind = 'line', y = ['TotalScore_13'], title = 'Total Score line plot sorted by Wealth Q3',xlabel = 'Unique responses', ylabel = 'TotalScore_13')
Out[44]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q3'}, xlabel='Unique responses', ylabel='TotalScore_13'>
No description has been provided for this image
In [45]:
# Trend line for Absolute Total Facet Score vs unique wealth reponses in Quartile 3

sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'TotalScore_13')
Out[45]:
<Axes: ylabel='TotalScore_13'>
No description has been provided for this image
In [46]:
# Trend line for Absolute Independent Facet Score vs wealth reponses in Quartile 3

fig1, ax4 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Certainty_3', fit_reg=True, ci=None, ax=ax4, label='Certainty_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax4, label='RiskComprehension_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax4, label='Numeracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax4, label='GraphLiteracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax4, label='Bayesianreasoning_1' )

ax4.set(ylabel='Scores', xlabel='wealth')
ax4.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [47]:
df4.loc[df4['Quartile Number']==4]
Out[47]:
ResponseId age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_% Quartile Number
482 rh3ida1re0oohl9crh3ida1s6gcynlil 30.5 2 1 4 5245899 3 0 1 1 0 100.000000 0.000000 33.333333 33.333333 0.0 5 38.461538 4
483 fa4fw88qzaxzjxw31rzchtfa4fw8gn8h 30.5 2 1 3 5500000 2 1 0 2 0 66.666667 33.333333 0.000000 66.666667 0.0 5 38.461538 4
484 8pwy5gq2mcpugpldx8pwy5javd312pha 30.5 2 1 3 5500000 1 1 0 3 0 33.333333 33.333333 0.000000 100.000000 0.0 5 38.461538 4
485 gdsir9721na619gpfsu9jqk7bi4rhpfs 30.5 2 2 3 5500000 1 3 2 2 1 33.333333 100.000000 66.666667 66.666667 100.0 9 69.230769 4
486 ks6mtnxelurtt3m3uihks6mtnlo5k2i3 30.5 2 2 1 5800000 0 0 2 3 0 0.000000 0.000000 66.666667 100.000000 0.0 5 38.461538 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
638 35zux4sc18rplyz3dc85z35zux4sdfm4 21.5 2 2 4 500000000 1 1 0 1 0 33.333333 33.333333 0.000000 33.333333 0.0 3 23.076923 4
639 wn9mk6m1k8o10l1twn9mk6b4z7l7yvhc 50.5 3 1 3 500000000 1 2 3 3 0 33.333333 66.666667 100.000000 100.000000 0.0 9 69.230769 4
640 oczyh23wpxpk4o0teoczy5zzzng3i6kk 30.5 2 2 6 500000000 1 2 3 3 0 33.333333 66.666667 100.000000 100.000000 0.0 9 69.230769 4
641 4udfypw7lf8mmgjmxr4d4udfym4sx1sf 40.5 3 1 3 500000000 0 0 0 2 1 0.000000 0.000000 0.000000 66.666667 100.0 3 23.076923 4
642 zcj7ldokhyo6217f9sriwizcj7ldodwt 40.5 3 2 3 600000000 1 3 1 1 0 33.333333 100.000000 33.333333 33.333333 0.0 6 46.153846 4

161 rows × 19 columns

In [48]:
# This graph is basically a line plot of the TotalScore_13 across fourth quartile unique responses which are ordered by Wealth

df4.loc[df4['Quartile Number']==4].plot(use_index = 'True' , kind = 'line', y = ['TotalScore_13'], title = 'Total Score line plot sorted by Wealth Q4',xlabel = 'Unique responses', ylabel = 'TotalScore_13')
Out[48]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q4'}, xlabel='Unique responses', ylabel='TotalScore_13'>
No description has been provided for this image
In [49]:
# Trend line for Absolute Total Facet Score vs unique wealth reponses in Quartile 4

sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'TotalScore_13')
Out[49]:
<Axes: ylabel='TotalScore_13'>
No description has been provided for this image
In [50]:
# Trend line for Absolute Independent Facet Score vs wealth reponses in Quartile 4

fig1, ax5 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Certainty_3', fit_reg=True, ci=None, ax=ax5, label='Certainty_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax5, label='RiskComprehension_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax5, label='Numeracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax5, label='GraphLiteracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax5, label='Bayesianreasoning_1' )

ax5.set(ylabel='Scores', xlabel='wealth')
ax5.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [ ]:
In [51]:
# Absolute Total Facet scores mean line plot from low to high Wealth

df4.groupby('Quartile Number')[['TotalScore_13']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[51]:
<matplotlib.legend.Legend at 0x1f81ccf0390>
No description has been provided for this image
In [52]:
# Absolute Facet scores mean line plot from low to high wealth

df4.groupby('Quartile Number')[['Certainty_3','RiskComprehension_3','Numeracy_3','GraphLiteracy_3','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[52]:
<matplotlib.legend.Legend at 0x1f81c38c290>
No description has been provided for this image
In [53]:
# Normalised Facet scores mean line plot from low to high wealth


df4.groupby('Quartile Number')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().plot( kind = 'line', title = 'Normalised Facet scores mean line plot from low to high wealth',  xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[53]:
<matplotlib.legend.Legend at 0x1f81c69a410>
No description has been provided for this image
In [54]:
# Normalised mean scores for each facet stacked

df4.groupby('Quartile Number')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = 'Normalised mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[54]:
<matplotlib.legend.Legend at 0x1f81cce0410>
No description has been provided for this image
In [55]:
# Normalised mean scores for each facet for each wealth response Quartile

df4.groupby('Quartile Number')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each wealth response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[55]:
<matplotlib.legend.Legend at 0x1f81ce91290>
No description has been provided for this image
In [56]:
# Trend line for Absolute Total Facet Score vs Quartiles

sns.regplot (data = df4, x = 'Quartile Number', y = 'TotalScore_13')
Out[56]:
<Axes: xlabel='Quartile Number', ylabel='TotalScore_13'>
No description has been provided for this image
In [57]:
# Trend line for Absolute Independent Facet Score vs Quartiles

fig2, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = 'Quartile Number', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax2, label='Certainty_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax2, label='RiskComprehension_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax2, label='Numeracy_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax2, label='GraphLiteracy_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax2, label='Bayesianreasoning_1' )

ax2.set(ylabel='Scores', xlabel='wealth')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [58]:
# Violine Plot for TotalScore_13 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'TotalScore_13')
Out[58]:
<Axes: xlabel='Quartile Number', ylabel='TotalScore_13'>
No description has been provided for this image
In [59]:
# Violine Plot for Certainty_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Certainty_3')
Out[59]:
<Axes: xlabel='Quartile Number', ylabel='Certainty_3'>
No description has been provided for this image
In [60]:
# Violine Plot for RiskComprehension_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'RiskComprehension_3')
Out[60]:
<Axes: xlabel='Quartile Number', ylabel='RiskComprehension_3'>
No description has been provided for this image
In [61]:
# Violine Plot for GraphLiteracy_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'GraphLiteracy_3')
Out[61]:
<Axes: xlabel='Quartile Number', ylabel='GraphLiteracy_3'>
No description has been provided for this image
In [62]:
# Violine Plot for Numeracy_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Numeracy_3')
Out[62]:
<Axes: xlabel='Quartile Number', ylabel='Numeracy_3'>
No description has been provided for this image
In [63]:
# Violine Plot for Bayesianreasoning_1 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Bayesianreasoning_1')
Out[63]:
<Axes: xlabel='Quartile Number', ylabel='Bayesianreasoning_1'>
No description has been provided for this image
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [64]:
# Data Frame 5 or df5 is an aggregate data on facet total scores and over all total score, along with  education data vs each response. 
# This data set is now sorted by education.

df5 = df2.sort_values(by = 'isced')
df5 = df5.reset_index(drop = True)

df5
Out[64]:
ResponseId age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_%
0 ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9 30.5 2 1 3 5000000 1 2 1 3 0 33.333333 66.666667 33.333333 100.000000 0.0 7 53.846154
1 ten07h1k6r3hqcfqten07isxh8g3c7sd 30.5 2 1 2 500000 0 0 0 3 0 0.000000 0.000000 0.000000 100.000000 0.0 3 23.076923
2 h5ngv0k7kcqx5i5qoftgxh5ngv0k7fvv 30.5 2 1 2 500000 1 2 0 0 0 33.333333 66.666667 0.000000 0.000000 0.0 3 23.076923
3 h5n6a65bgsz5w1mi1inh5n6a7l54vger 40.5 3 1 2 30000 1 1 3 0 0 33.333333 33.333333 100.000000 0.000000 0.0 5 38.461538
4 tftzv0n6j9gvvxwm68xtftzv0hvv1e1m 30.5 2 1 6 20000000 0 0 0 0 0 0.000000 0.000000 0.000000 0.000000 0.0 0 0.000000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
638 y139mcxc619h0ooosy139muk14mpfpvm 30.5 2 3 1 0 3 1 0 2 0 100.000000 33.333333 0.000000 66.666667 0.0 6 46.153846
639 mj52mssq166g3wubcnkmj520tfdzf8oz 40.5 3 3 1 2000 1 1 1 2 0 33.333333 33.333333 33.333333 66.666667 0.0 5 38.461538
640 a31qwuv5yj3n5tlmea3183d7y7salhcl 30.5 2 3 1 5000000 0 1 0 2 0 0.000000 33.333333 0.000000 66.666667 0.0 3 23.076923
641 870h2qs77chp1cd5q870n4fnkjbrxezk 21.5 2 3 2 150000 0 1 0 0 0 0.000000 33.333333 0.000000 0.000000 0.0 1 7.692308
642 t1hiz7zjzou3pfd59pqt1hizrglay7e5 40.5 3 3 1 200000 1 1 2 1 0 33.333333 33.333333 66.666667 33.333333 0.0 5 38.461538

643 rows × 18 columns

In [65]:
# since we didnt drop any values, the dataset is essentially the same as df2, but sorted
In [66]:
# Mean of each facet as a % for each category of isced

df5.groupby('isced')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean()
Out[66]:
Certainty_% RiskComprehension_% Numeracy_% GraphLiteracy_% Bayesianreasoning_%
isced
1 19.423868 39.588477 56.707819 40.000000 24.444444
2 19.444444 41.512346 59.259259 39.351852 29.629630
3 31.818182 30.303030 53.030303 33.333333 9.090909
In [67]:
# Count of responses for each category isced

df5.groupby('isced')[['ResponseId']].count()
Out[67]:
ResponseId
isced
1 405
2 216
3 22
In [68]:
# Absolute Total Facet scores mean line plot from low to high education

df5.groupby('isced')[['TotalScore_13']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[68]:
<matplotlib.legend.Legend at 0x1f81ed9e8d0>
No description has been provided for this image
In [69]:
# Absolute Facet scores mean line plot from low to high edu

df5.groupby('isced')[['Certainty_3','RiskComprehension_3','Numeracy_3','GraphLiteracy_3','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[69]:
<matplotlib.legend.Legend at 0x1f81eeb15d0>
No description has been provided for this image
In [70]:
# Normalised Facet scores mean line plot from low to high edu


df5.groupby('isced')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().plot( kind = 'line', title = 'Normalised Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[70]:
<matplotlib.legend.Legend at 0x1f81ef63450>
No description has been provided for this image
In [71]:
# Normalised mean scores for each facet stacked

df5.groupby('isced')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = 'Normalised mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[71]:
<matplotlib.legend.Legend at 0x1f81efc2790>
No description has been provided for this image
In [72]:
# Normalised mean scores for each facet for each edu response category

df5.groupby('isced')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each edu response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[72]:
<matplotlib.legend.Legend at 0x1f81f2b1750>
No description has been provided for this image
In [73]:
# Trend line for Absolute Total Facet Score vs edu (isced) reponses

sns.regplot (data = df5, x = 'isced', y = 'TotalScore_13')
Out[73]:
<Axes: xlabel='isced', ylabel='TotalScore_13'>
No description has been provided for this image
In [74]:
# Trend line for Absolute Independent Facet Score vs edu (isced) reponses

fig, ax6 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df5, x = 'isced', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax6, label='Certainty_3')
sns.regplot (data = df5, x = 'isced', y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax6, label='RiskComprehension_3')
sns.regplot (data = df5, x = 'isced', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax6, label='Numeracy_3')
sns.regplot (data = df5, x = 'isced', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax6, label='GraphLiteracy_3')
sns.regplot (data = df5, x = 'isced', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax6, label='Bayesianreasoning_1' )

ax6.set(ylabel='Scores', xlabel='isced')
ax6.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [75]:
# Violine Plot for TotalScore_13 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'TotalScore_13')
Out[75]:
<Axes: xlabel='isced', ylabel='TotalScore_13'>
No description has been provided for this image
In [76]:
# Violine Plot for Certainty_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Certainty_3')
Out[76]:
<Axes: xlabel='isced', ylabel='Certainty_3'>
No description has been provided for this image
In [77]:
# Violine Plot for RiskComprehension_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'RiskComprehension_3')
Out[77]:
<Axes: xlabel='isced', ylabel='RiskComprehension_3'>
No description has been provided for this image
In [78]:
# Violine Plot for GraphLiteracy_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'GraphLiteracy_3')
Out[78]:
<Axes: xlabel='isced', ylabel='GraphLiteracy_3'>
No description has been provided for this image
In [79]:
# Violine Plot for Numeracy_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Numeracy_3')
Out[79]:
<Axes: xlabel='isced', ylabel='Numeracy_3'>
No description has been provided for this image
In [80]:
# Violine Plot for TotalScore_13 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Bayesianreasoning_1')
Out[80]:
<Axes: xlabel='isced', ylabel='Bayesianreasoning_1'>
No description has been provided for this image
In [81]:
# Descriptive stats for the data set, isced = 1
# NA values of wealth are removed

df6 = df5.dropna(axis = 0, subset = 'wealth')
# df6.drop(df6[df6['wealth'] == 3500000].index, inplace = True)
df6.loc[df6['isced']==1][['wealth']].describe()
Out[81]:
wealth
count 4.050000e+02
mean 1.220912e+07
std 4.537261e+07
min 0.000000e+00
25% 5.000000e+05
50% 1.700000e+06
75% 7.500000e+06
max 5.000000e+08
In [82]:
# Descriptive stats for the data set, isced = 2

df6.loc[df6['isced']==2][['wealth']].describe()
Out[82]:
wealth
count 2.160000e+02
mean 1.355720e+07
std 6.621355e+07
min 0.000000e+00
25% 3.375000e+05
50% 9.999500e+05
75% 5.000000e+06
max 6.000000e+08
In [83]:
# Descriptive stats for the data set, isced = 3

df6.loc[df6['isced']==3][['wealth']].describe()
Out[83]:
wealth
count 2.200000e+01
mean 3.038705e+06
std 7.097479e+06
min 0.000000e+00
25% 3.500000e+04
50% 2.000000e+05
75% 1.650125e+06
max 3.000000e+07
In [84]:
# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='Wealth')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [85]:
# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='Wealth')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [86]:
# Trend line for Absolute Certainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty_3', xlabel='Wealth')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [87]:
# Trend line for Absolute Uncertainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='RiskComprehension_3', xlabel='Wealth')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [88]:
# Trend line for Absolute Number Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Numeracy_3', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [89]:
# Trend line for Absolute Graph Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='GraphLiteracy_3', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [90]:
# Trend line for Absolute Bayesian Reasoning Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Bayesianreasoning_1', xlabel='Wealth')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [ ]:
In [ ]:
In [ ]:
In [91]:
# Descriptive stats for the data set, isced = 1
# NA values of income are removed

df7 = df5
df7.drop(df7[df7['income'] == 7].index, inplace = True)

df7.loc[df7['isced']==1].describe()
Out[91]:
age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_%
count 405.000000 405.000000 405.0 405.000000 4.050000e+02 405.000000 405.000000 405.00000 405.000000 405.000000 405.000000 405.000000 405.000000 405.000000 405.000000 405.000000 405.000000
mean 35.337037 2.446914 1.0 2.760494 1.220912e+07 0.582716 1.187654 1.20000 1.701235 0.244444 19.423868 39.588477 40.000000 56.707819 24.444444 4.916049 37.815764
std 9.171529 0.563117 0.0 1.338471 4.537261e+07 0.745413 0.889624 0.96061 1.032695 0.430289 24.847113 29.654143 32.020346 34.423165 43.028887 2.212222 17.017090
min 21.500000 2.000000 1.0 1.000000 0.000000e+00 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 30.500000 2.000000 1.0 2.000000 5.000000e+05 0.000000 1.000000 0.00000 1.000000 0.000000 0.000000 33.333333 0.000000 33.333333 0.000000 3.000000 23.076923
50% 30.500000 2.000000 1.0 2.000000 1.700000e+06 0.000000 1.000000 1.00000 2.000000 0.000000 0.000000 33.333333 33.333333 66.666667 0.000000 5.000000 38.461538
75% 40.500000 3.000000 1.0 3.000000 7.500000e+06 1.000000 2.000000 2.00000 3.000000 0.000000 33.333333 66.666667 66.666667 100.000000 0.000000 7.000000 53.846154
max 70.500000 4.000000 1.0 6.000000 5.000000e+08 3.000000 3.000000 3.00000 3.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 11.000000 84.615385
In [92]:
df7.loc[df7['isced']==2].describe()
Out[92]:
age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_%
count 216.000000 216.000000 216.0 216.000000 2.160000e+02 216.000000 216.000000 216.000000 216.000000 216.000000 216.000000 216.000000 216.000000 216.000000 216.000000 216.000000 216.000000
mean 33.634259 2.375000 2.0 2.166667 1.355720e+07 0.583333 1.245370 1.180556 1.777778 0.296296 19.444444 41.512346 39.351852 59.259259 29.629630 5.083333 39.102564
std 10.135158 0.564966 0.0 1.204642 6.621355e+07 0.754829 0.940053 0.930012 0.958034 0.457684 25.160980 31.335096 31.000403 31.934472 45.768394 2.208335 16.987194
min 21.500000 2.000000 2.0 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 30.500000 2.000000 2.0 1.000000 3.375000e+05 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 33.333333 0.000000 4.000000 30.769231
50% 30.500000 2.000000 2.0 2.000000 9.999500e+05 0.000000 1.000000 1.000000 2.000000 0.000000 0.000000 33.333333 33.333333 66.666667 0.000000 5.000000 38.461538
75% 40.500000 3.000000 2.0 2.000000 5.000000e+06 1.000000 2.000000 2.000000 3.000000 1.000000 33.333333 66.666667 66.666667 100.000000 100.000000 6.000000 46.153846
max 60.500000 4.000000 2.0 6.000000 6.000000e+08 3.000000 3.000000 3.000000 3.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 10.000000 76.923077
In [93]:
df7.loc[df7['isced']==3].describe()
Out[93]:
age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_%
count 22.000000 22.000000 22.0 22.000000 2.200000e+01 22.000000 22.000000 22.000000 22.000000 22.000000 22.000000 22.000000 22.000000 22.000000 22.000000 22.000000 22.000000
mean 36.681818 2.590909 3.0 1.454545 3.038705e+06 0.954545 0.909091 1.000000 1.590909 0.090909 31.818182 30.303030 33.333333 53.030303 9.090909 4.545455 34.965035
std 14.384636 0.666125 0.0 0.857864 7.097479e+06 0.898532 0.750180 1.112697 1.181568 0.294245 29.951058 25.006012 37.089909 39.385614 29.424494 2.482842 19.098781
min 21.500000 2.000000 3.0 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 23.750000 2.000000 3.0 1.000000 3.500000e+04 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 33.333333 0.000000 3.000000 23.076923
50% 35.500000 2.500000 3.0 1.000000 2.000000e+05 1.000000 1.000000 1.000000 1.500000 0.000000 33.333333 33.333333 33.333333 50.000000 0.000000 5.000000 38.461538
75% 40.500000 3.000000 3.0 1.750000 1.650125e+06 1.000000 1.000000 2.000000 3.000000 0.000000 33.333333 33.333333 66.666667 100.000000 0.000000 6.000000 46.153846
max 70.500000 4.000000 3.0 4.000000 3.000000e+07 3.000000 2.000000 3.000000 3.000000 1.000000 100.000000 66.666667 100.000000 100.000000 100.000000 9.000000 69.230769
In [94]:
# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==1]['TotalScore_13'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==2]['TotalScore_13'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==3]['TotalScore_13'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='INCOME')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [95]:
# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='INCOME')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [96]:
# Trend line for Absolute Certainty Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty_3', xlabel='INCOME')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [97]:
# Trend line for Absolute Risk Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='RiskComprehension_3', xlabel='INCOME')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [98]:
# Trend line for Absolute Number Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Numeracy_3', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [99]:
# Trend line for Absolute Graph Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='GraphLiteracy_3', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [100]:
# Trend line for Absolute Bayesian Reasoning Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Bayesianreasoning_1', xlabel='INCOME')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [ ]:
In [ ]:
In [ ]:
In [101]:
df7
Out[101]:
ResponseId age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_%
0 ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9 30.5 2 1 3 5000000 1 2 1 3 0 33.333333 66.666667 33.333333 100.000000 0.0 7 53.846154
1 ten07h1k6r3hqcfqten07isxh8g3c7sd 30.5 2 1 2 500000 0 0 0 3 0 0.000000 0.000000 0.000000 100.000000 0.0 3 23.076923
2 h5ngv0k7kcqx5i5qoftgxh5ngv0k7fvv 30.5 2 1 2 500000 1 2 0 0 0 33.333333 66.666667 0.000000 0.000000 0.0 3 23.076923
3 h5n6a65bgsz5w1mi1inh5n6a7l54vger 40.5 3 1 2 30000 1 1 3 0 0 33.333333 33.333333 100.000000 0.000000 0.0 5 38.461538
4 tftzv0n6j9gvvxwm68xtftzv0hvv1e1m 30.5 2 1 6 20000000 0 0 0 0 0 0.000000 0.000000 0.000000 0.000000 0.0 0 0.000000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
638 y139mcxc619h0ooosy139muk14mpfpvm 30.5 2 3 1 0 3 1 0 2 0 100.000000 33.333333 0.000000 66.666667 0.0 6 46.153846
639 mj52mssq166g3wubcnkmj520tfdzf8oz 40.5 3 3 1 2000 1 1 1 2 0 33.333333 33.333333 33.333333 66.666667 0.0 5 38.461538
640 a31qwuv5yj3n5tlmea3183d7y7salhcl 30.5 2 3 1 5000000 0 1 0 2 0 0.000000 33.333333 0.000000 66.666667 0.0 3 23.076923
641 870h2qs77chp1cd5q870n4fnkjbrxezk 21.5 2 3 2 150000 0 1 0 0 0 0.000000 33.333333 0.000000 0.000000 0.0 1 7.692308
642 t1hiz7zjzou3pfd59pqt1hizrglay7e5 40.5 3 3 1 200000 1 1 2 1 0 33.333333 33.333333 66.666667 33.333333 0.0 5 38.461538

643 rows × 18 columns

In [102]:
# Since we already have a classification for Age groups in the form of age_rec, we will use that.
# We will also use median of age to see if it yields any relevant results, as instructed.

# AXES to be used = Age or age groups, ISCED, Income

# Age groups = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 59 y/o
# Age group 4 = 60 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)
In [103]:
df7.loc[(df7['age_rec']==2)].describe()
Out[103]:
age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_%
count 393.000000 393.0 393.000000 393.000000 3.930000e+02 393.000000 393.000000 393.000000 393.000000 393.000000 393.000000 393.000000 393.000000 393.000000 393.000000 393.000000 393.000000
mean 28.393130 2.0 1.422392 2.394402 9.461581e+06 0.552163 1.195929 1.165394 1.712468 0.244275 18.405428 39.864292 38.846480 57.082273 24.427481 4.870229 37.463300
std 3.815754 0.0 0.548381 1.241160 4.116973e+07 0.737447 0.917396 0.931656 1.028310 0.430204 24.581570 30.579878 31.055203 34.276987 43.020409 2.154955 16.576576
min 21.500000 2.0 1.000000 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 30.500000 2.0 1.000000 2.000000 3.000000e+05 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 33.333333 0.000000 3.000000 23.076923
50% 30.500000 2.0 1.000000 2.000000 1.000000e+06 0.000000 1.000000 1.000000 2.000000 0.000000 0.000000 33.333333 33.333333 66.666667 0.000000 5.000000 38.461538
75% 30.500000 2.0 2.000000 3.000000 5.000000e+06 1.000000 2.000000 2.000000 3.000000 0.000000 33.333333 66.666667 66.666667 100.000000 0.000000 7.000000 53.846154
max 30.500000 2.0 3.000000 6.000000 5.000000e+08 3.000000 3.000000 3.000000 3.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 10.000000 76.923077
In [104]:
df7.loc[(df7['age_rec']==3)].describe()
Out[104]:
age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_%
count 225.000000 225.0 225.000000 225.000000 2.250000e+02 225.000000 225.000000 225.000000 225.000000 225.000000 225.000000 225.000000 225.000000 225.000000 225.000000 225.000000 225.000000
mean 42.900000 3.0 1.360000 2.768889 1.742734e+07 0.662222 1.195556 1.177778 1.746667 0.271111 22.074074 39.851852 39.259259 58.222222 27.111111 5.053333 38.871795
std 4.280354 0.0 0.558378 1.423624 6.999865e+07 0.797118 0.889913 0.988525 0.983253 0.445524 26.570594 29.663753 32.950848 32.775087 44.552448 2.327399 17.903069
min 40.500000 3.0 1.000000 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 40.500000 3.0 1.000000 2.000000 3.800000e+05 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 33.333333 0.000000 33.333333 0.000000 3.000000 23.076923
50% 40.500000 3.0 1.000000 2.000000 2.500000e+06 0.000000 1.000000 1.000000 2.000000 0.000000 0.000000 33.333333 33.333333 66.666667 0.000000 5.000000 38.461538
75% 40.500000 3.0 2.000000 3.000000 7.800000e+06 1.000000 2.000000 2.000000 3.000000 1.000000 33.333333 66.666667 66.666667 100.000000 100.000000 7.000000 53.846154
max 50.500000 3.0 3.000000 6.000000 6.000000e+08 3.000000 3.000000 3.000000 3.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 11.000000 84.615385
In [105]:
df7.loc[(df7['age_rec']==4)].describe()
Out[105]:
age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_%
count 25.000000 25.0 25.000000 25.000000 2.500000e+01 25.000000 25.000000 25.000000 25.000000 25.000000 25.000000 25.000000 25.000000 25.000000 25.000000 25.000000 25.000000
mean 62.900000 4.0 1.520000 2.160000 1.201400e+07 0.680000 1.240000 1.600000 1.680000 0.320000 22.666667 41.333333 53.333333 56.000000 32.000000 5.520000 42.461538
std 4.358899 0.0 0.653197 1.374773 1.651268e+07 0.627163 0.830662 0.957427 1.069268 0.476095 20.905431 27.688746 31.914237 35.642255 47.609523 2.200757 16.928903
min 60.500000 4.0 1.000000 1.000000 1.000000e+05 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000 15.384615
25% 60.500000 4.0 1.000000 1.000000 2.000000e+06 0.000000 1.000000 1.000000 1.000000 0.000000 0.000000 33.333333 33.333333 33.333333 0.000000 4.000000 30.769231
50% 60.500000 4.0 1.000000 2.000000 5.000000e+06 1.000000 1.000000 2.000000 1.000000 0.000000 33.333333 33.333333 66.666667 33.333333 0.000000 5.000000 38.461538
75% 60.500000 4.0 2.000000 2.000000 1.000000e+07 1.000000 2.000000 2.000000 3.000000 1.000000 33.333333 66.666667 66.666667 100.000000 100.000000 8.000000 61.538462
max 70.500000 4.0 3.000000 6.000000 5.250000e+07 2.000000 3.000000 3.000000 3.000000 1.000000 66.666667 100.000000 100.000000 100.000000 100.000000 9.000000 69.230769
In [ ]:
In [106]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_14', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [107]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income']).mean(numeric_only=True)['TotalScore_13'], yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['TotalScore_13'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_14', xlabel='INCOME 18 to 35',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='INCOME 36 to 59',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income']).mean(numeric_only=True)['TotalScore_13'], yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['TotalScore_13'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='INCOME 60 to 75',  yticks = np.arange(5, 16 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income']).mean(numeric_only=True)['TotalScore_13'], yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['TotalScore_13'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [108]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Certainty_3', xlabel='INCOME 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['Certainty_3'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['Certainty_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Certainty_3', xlabel='INCOME 36 to 59',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['Certainty_3'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['Certainty_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Certainty_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Certainty_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Certainty_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Certainty_3', xlabel='INCOME 60 to 75',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['Certainty_3'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['Certainty_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [109]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='RiskComprehension_3', xlabel='INCOME 18 to 35',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['RiskComprehension_3'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['RiskComprehension_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='RiskComprehension_3', xlabel='INCOME 36 to 59',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['RiskComprehension_3'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['RiskComprehension_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='RiskComprehension_3', xlabel='INCOME 60 to 75',  yticks = np.arange(0, 3 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['RiskComprehension_3'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['RiskComprehension_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [110]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Numeracy_3', xlabel='INCOME 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['Numeracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['Numeracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Numeracy_3', xlabel='INCOME 36 to 59',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['Numeracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['Numeracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Numeracy_3', xlabel='INCOME 60 to 75',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['Numeracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['Numeracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [111]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='GraphLiteracy_3', xlabel='INCOME 18 to 35',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='GraphLiteracy_3', xlabel='INCOME 36 to 59',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='GraphLiteracy_3', xlabel='INCOME 60 to 75',  yticks = np.arange(0, 4 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [112]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Bayesianreasoning_1', xlabel='INCOME 18 to 35',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Bayesianreasoning_1', xlabel='INCOME 36 to 59',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Bayesianreasoning_1', xlabel='INCOME 60 to 75',  yticks = np.arange(0, 5 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [113]:
# Age weighted mean method
In [114]:
df2["age"].value_counts()
Out[114]:
age
30.5    301
40.5    171
21.5     92
50.5     54
60.5     19
70.5      6
Name: count, dtype: int64
In [115]:
age_m = (30.5 * 301 + 40.5*171 + 21.5*92 + 50.5*54 + 60.5*19+ 70.6*6)/(301+171+92+54+19+6)
age_m
Out[115]:
34.811975116640745
In [ ]:
In [116]:
df7["income"].value_counts()
Out[116]:
income
2    281
1    125
3    121
4     46
5     36
6     34
Name: count, dtype: int64
In [ ]:
In [ ]:
In [117]:
df7.loc[(df7['age'] < age_m)].describe()
Out[117]:
age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_%
count 393.000000 393.0 393.000000 393.000000 3.930000e+02 393.000000 393.000000 393.000000 393.000000 393.000000 393.000000 393.000000 393.000000 393.000000 393.000000 393.000000 393.000000
mean 28.393130 2.0 1.422392 2.394402 9.461581e+06 0.552163 1.195929 1.165394 1.712468 0.244275 18.405428 39.864292 38.846480 57.082273 24.427481 4.870229 37.463300
std 3.815754 0.0 0.548381 1.241160 4.116973e+07 0.737447 0.917396 0.931656 1.028310 0.430204 24.581570 30.579878 31.055203 34.276987 43.020409 2.154955 16.576576
min 21.500000 2.0 1.000000 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 30.500000 2.0 1.000000 2.000000 3.000000e+05 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 33.333333 0.000000 3.000000 23.076923
50% 30.500000 2.0 1.000000 2.000000 1.000000e+06 0.000000 1.000000 1.000000 2.000000 0.000000 0.000000 33.333333 33.333333 66.666667 0.000000 5.000000 38.461538
75% 30.500000 2.0 2.000000 3.000000 5.000000e+06 1.000000 2.000000 2.000000 3.000000 0.000000 33.333333 66.666667 66.666667 100.000000 0.000000 7.000000 53.846154
max 30.500000 2.0 3.000000 6.000000 5.000000e+08 3.000000 3.000000 3.000000 3.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 10.000000 76.923077
In [118]:
df7.loc[(df7['age'] > age_m)].describe()
Out[118]:
age age_rec isced income wealth Certainty_3 RiskComprehension_3 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_13 TotalScore_%
count 250.000000 250.000000 250.000000 250.000000 2.500000e+02 250.000000 250.000000 250.000000 250.000000 250.000000 250.000000 250.000000 250.000000 250.000000 250.00000 250.000000 250.000000
mean 44.900000 3.100000 1.376000 2.708000 1.688600e+07 0.664000 1.200000 1.220000 1.740000 0.276000 22.133333 40.000000 40.666667 58.000000 27.60000 5.100000 39.230769
std 7.379555 0.300602 0.569139 1.427898 6.660925e+07 0.780732 0.882676 0.991733 0.990112 0.447914 26.024394 29.422525 33.057763 33.003725 44.79135 2.315046 17.808046
min 40.500000 3.000000 1.000000 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000
25% 40.500000 3.000000 1.000000 2.000000 5.000000e+05 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 33.333333 0.000000 33.333333 0.00000 3.000000 23.076923
50% 40.500000 3.000000 1.000000 2.000000 2.750000e+06 0.500000 1.000000 1.000000 2.000000 0.000000 16.666667 33.333333 33.333333 66.666667 0.00000 5.000000 38.461538
75% 50.500000 3.000000 2.000000 3.000000 8.000000e+06 1.000000 2.000000 2.000000 3.000000 1.000000 33.333333 66.666667 66.666667 100.000000 100.00000 7.000000 53.846154
max 70.500000 4.000000 3.000000 6.000000 6.000000e+08 3.000000 3.000000 3.000000 3.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.00000 11.000000 84.615385
In [ ]:
In [119]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [120]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['TotalScore_13'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['TotalScore_13'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] > age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] > age_m)  ].groupby(['income'])['TotalScore_13'].mean(), yerr = df7.loc[ (df7['age'] > age_m) ].groupby(['income'])['TotalScore_13'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Total Scores_19', xlabel='INCOME for > median age',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [121]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['Certainty_3'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['Certainty_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Certainty_3', xlabel='INCOME for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] > age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] > age_m)  ].groupby(['income'])['Certainty_3'].mean(), yerr = df7.loc[ (df7['age'] > age_m) ].groupby(['income'])['Certainty_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Certainty_3', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [122]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['RiskComprehension_3'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['RiskComprehension_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='RiskComprehension_3', xlabel='INCOME for < median age',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] > age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] > age_m)  ].groupby(['income'])['RiskComprehension_3'].mean(), yerr = df7.loc[ (df7['age'] > age_m) ].groupby(['income'])['RiskComprehension_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='RiskComprehension_3', xlabel='INCOME for > median age',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [123]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['Numeracy_3'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['Numeracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Numeracy_3', xlabel='INCOME for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] > age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] > age_m)  ].groupby(['income'])['Numeracy_3'].mean(), yerr = df7.loc[ (df7['age'] > age_m) ].groupby(['income'])['Numeracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Numeracy_3', xlabel='INCOME for > median age',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [124]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='GraphLiteracy_3', xlabel='INCOME for < median age',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] > age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] > age_m)  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age'] > age_m) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='GraphLiteracy_3', xlabel='INCOME for > median age',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [125]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for < median age',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] > age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] > age_m)  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age'] > age_m) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for > median age',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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